| Description |
1 online resource (522 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Author Biographies -- Summary of Notation -- CHAPTER 1: Overview -- 1.1. LEARNING REINFORCEMENT LEARNING -- 1.2. WHAT YOU WILL LEARN FROM THIS BOOK -- 1.3. EXPECTED BACKGROUND TO READ THIS BOOK -- 1.4. DECLUTTERING THE JARGON LINKED TO REINFORCEMENT LEARNING -- 1.5. INTRODUCTION TO THE MARKOV DECISION PROCESS (MDP) FRAMEWORK -- 1.6. REAL-WORLD PROBLEMS THAT FIT THE MDP FRAMEWORK -- 1.7. THE INHERENT DIFFICULTY IN SOLVING MDPS -- 1.8. VALUE FUNCTION, BELLMAN EQUATIONS, DYNAMIC PROGRAMMING AND RL |
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1.9. OUTLINE OF CHAPTERS -- 1.9.1. Module I: Processes and Planning Algorithms -- 1.9.2. Module II: Modeling Financial Applications -- 1.9.3. Module III: Reinforcement Learning Algorithms -- 1.9.4. Module IV: Finishing Touches -- 1.9.5. Short Appendix Chapters -- CHAPTER 2: Programming and Design -- 2.1. CODE DESIGN -- 2.2. ENVIRONMENT SETUP -- 2.3. CLASSES AND INTERFACES -- 2.3.1. A Distribution Interface -- 2.3.2. A Concrete Distribution -- 2.3.2.1. Dataclasses -- 2.3.2.2. Immutability -- 2.3.3. Checking Types -- 2.3.3.1. Static Typing -- 2.3.4. Type Variables -- 2.3.5. Functionality |
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2.4. ABSTRACTING OVER COMPUTATION -- 2.4.1. First-Class Functions -- 2.4.1.1. Lambdas -- 2.4.2. Iterative Algorithms -- 2.4.2.1. Iterators and Generators -- 2.5. KEY TAKEAWAYS FROM THIS CHAPTER -- MODULE I: Processes and Planning Algorithms -- Chapter 3: Markov Processes -- 3.1. THE CONCEPT OF STATE IN A PROCESS -- 3.2. UNDERSTANDING MARKOV PROPERTY FROM STOCK PRICE EXAMPLES -- 3.3. FORMAL DEFINITIONS FOR MARKOV PROCESSES -- 3.3.1. Starting States -- 3.3.2. Terminal States -- 3.3.3. Markov Process Implementation -- 3.4. STOCK PRICE EXAMPLES MODELED AS MARKOV PROCESSES |
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3.5. FINITE MARKOV PROCESSES -- 3.6. SIMPLE INVENTORY EXAMPLE -- 3.7. STATIONARY DISTRIBUTION OF A MARKOV PROCESS -- 3.8. FORMALISM OF MARKOV REWARD PROCESSES -- 3.9. SIMPLE INVENTORY EXAMPLE AS A MARKOV REWARD PROCESS -- 3.10. FINITE MARKOV REWARD PROCESSES -- 3.11. SIMPLE INVENTORY EXAMPLE AS A FINITE MARKOV REWARD PROCESS -- 3.12. VALUE FUNCTION OF A MARKOV REWARD PROCESS -- 3.13. SUMMARY OF KEY LEARNINGS FROM THIS CHAPTER -- Chapter 4: Markov Decision Processes -- 4.1. SIMPLE INVENTORY EXAMPLE: HOW MUCH TO ORDER? -- 4.2. THE DIFFICULTY OF SEQUENTIAL DECISIONING UNDER UNCERTAINTY |
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4.3. FORMAL DEFINITION OF A MARKOV DECISION PROCESS -- 4.4. POLICY -- 4.5. [MARKOV DECISION PROCESS, POLICY] := MARKOV REWARD PROCESS -- 4.6. SIMPLE INVENTORY EXAMPLE WITH UNLIMITED CAPACITY (INFINITE STATE/ACTION SPACE) -- 4.7. FINITE MARKOV DECISION PROCESSES -- 4.8. SIMPLE INVENTORY EXAMPLE AS A FINITE MARKOV DECISION PROCESS -- 4.9. MDP VALUE FUNCTION FOR A FIXED POLICY -- 4.10. OPTIMAL VALUE FUNCTION AND OPTIMAL POLICIES -- 4.11. VARIANTS AND EXTENSIONS OF MDPS -- 4.11.1. Size of Spaces and Discrete versus Continuous -- 4.11.1.1. State Space -- 4.11.1.2. Action Space -- 4.11.1.3. Time Steps |
| Notes |
Description based upon print version of record |
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4.11.2. Partially-Observable Markov Decision Processes (POMDPs) |
| Form |
Electronic book
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| Author |
Jelvis, Tikhon
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| ISBN |
9781000801101 |
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1000801101 |
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